If your organization uses AI tools built by someone else, regulators are no longer satisfied with a standard vendor questionnaire. In 2026, third-party AI risk assessment has become a distinct compliance discipline, and the bar is rising fast across financial services, insurance, and any sector touched by state-level AI legislation.
The problem is straightforward: most vendor due diligence programs were designed for SaaS platforms and data processors, not for systems that make or influence decisions about lending, underwriting, hiring, or customer eligibility. AI vendors introduce a category of risk that traditional procurement checklists were never built to capture.
The Regulatory Pressure Is Coming From Multiple Directions
Three regulatory developments are converging on the same expectation: organizations must demonstrate structured, documented oversight of the AI systems they deploy, even when those systems are built and maintained by third parties.
NCUA and credit union AI oversight. The National Credit Union Administration updated its AI resource hub in late 2025 to consolidate guidance for federally insured credit unions evaluating third-party AI vendors. NCUA examiners are now referencing NIST AI Risk Management Framework benchmarks and existing third-party oversight guidance when assessing how credit unions govern AI solutions. The expectation is clear: AI vendor management must sit within your existing risk management structure, not off to the side in an IT pilot.
NAIC Model Bulletin adoption. As of April 2026, 23 states plus the District of Columbia have adopted the NAIC AI Model Bulletin, which sets expectations for insurers using AI in underwriting, claims, and pricing. A recent industry survey found that only 24 percent of insurance executives are confident they could pass an independent AI governance review within 90 days. The gap between adoption of AI tools and documentation of their governance is widening, and state insurance regulators are paying attention.
Colorado AI Act deployer obligations. Colorado's SB24-205, with a compliance date of June 30, 2026, requires deployers of high-risk AI systems to implement a risk management policy that identifies and mitigates known risks of algorithmic discrimination. Deployers must conduct impact assessments, notify consumers when AI influences consequential decisions, and maintain documentation referencing recognized frameworks like NIST AI RMF or ISO/IEC 42001. While enforcement is currently stayed pending AG rulemaking, the compliance architecture is set and organizations operating in Colorado should be building it now.
What Traditional Vendor Reviews Miss
A standard vendor risk assessment typically covers data security, uptime SLAs, and business continuity. AI systems demand a different set of questions:
Transparency. Can the vendor explain how the model reaches its outputs? Is there documentation of training data sources, model architecture, and known limitations? Without this, your organization cannot conduct meaningful impact assessments as required under Colorado's law or expected under NCUA and NAIC guidance.
Bias testing and fairness. Has the vendor tested for algorithmic discrimination across protected classes? Can they provide results? The Colorado AI Act specifically requires deployers to address risks of algorithmic discrimination, which means you need evidence from your vendors, not just assurances.
Model lifecycle management. AI systems change over time through retraining, fine-tuning, and data drift. Your due diligence process needs to account for how the vendor monitors model performance post-deployment and how they communicate material changes to deployers.
Audit trail. Regulators increasingly expect a documented chain of governance decisions. If an examiner asks why you chose a particular AI vendor for a lending or underwriting function, you need more than a procurement approval form.
Building a Due Diligence Process That Holds Up
Organizations that are ahead of this curve are doing a few things differently.
First, they are maintaining a centralized AI inventory. You cannot govern what you have not cataloged. Every AI system in use, whether built internally or provided by a vendor, should be documented with its purpose, risk classification, and the business function it supports.
Second, they are requiring standardized transparency disclosures from vendors. Rather than accepting marketing materials as evidence of responsible AI practices, procurement and compliance teams are asking for structured documentation: model cards, bias audit results, data provenance records, and incident response protocols.
Third, they are benchmarking vendor disclosures against recognized standards. The NIST AI RMF, ISO 42001, and frameworks like the MIT AI Risk Repository provide criteria that make vendor claims testable rather than aspirational. Independent AI transparency ratings, such as those published in the AI Clear registry, offer a way to compare how well organizations disclose their AI practices against a consistent, public rubric anchored to these same standards.
The Cost of Waiting
The regulatory trajectory is unambiguous. Whether you are a credit union compliance officer preparing for your next NCUA exam, an insurance underwriter navigating NAIC expectations in your state, or a procurement lead at a Colorado enterprise assessing vendor risk, the question is no longer whether you need a formal AI vendor due diligence process. It is whether the one you have is specific enough to satisfy the regulators who are already asking.
The organizations that build this capacity now will spend less time scrambling when enforcement actions begin. Those that treat AI vendor oversight as an extension of their existing checkbox process will find that the checkbox has moved.
Ready to see how your AI vendors measure up? Visit the AI Clear public registry to explore transparency ratings for AI systems, or request a rating for a vendor in your supply chain. The registry is free to search and built on a 49-criteria rubric aligned with NIST AI RMF, ISO 42001, and the MIT AI Risk Repository.
See where your company stands
AI Clear scores companies on AI transparency. Search the registry or request your scorecard.